本书从自然语言图像中面临的图像增强与修复、自然图像语义分割算法展开，深入理解自然图像。接着，进行机器学习算法、深度学习算法的建模实现遥感图像的语义分割问题。最后，通过算法，实现下游任务。
本书可供从事遥感图像处理、机器学习研究的技术人员阅读，也可供大专院校相关专业的师生参考。

1 Blind Image Deblurring with Joint Extreme Channels and Lo-Regularized Intensity and Gradient Priors
2 Non-Blind Deconvolution with L,-Norm of High-Frequency Fidelity
3 A Novel Framework Method for Non-Blind Deconvolution Using Subspace Images Priors
4 Image Reconstruction with an Edge-Preserving Filtering Prior
5 Compressed Sensing Image with Joint Image-Level and Patch-Level Priors
6 Mixed Noise Removal Based on a Novel Non-Parametric Bayesian Sparse Outlier Model
7 DewaterNet: A Fusion Adversarial Real Underwater Image Enhancement Network
8 Underwater Image Enhancement Using a Multi-Scale Dense Generative Adversarial Network
9 Compressed Sensing Image via a Multi-scale Dilated Residual Convolution Network
10 Attention Guided Global Enhancement and Local Refinement Network for Semantic Segmentation
11 Land Cover Classification Method by SVM and Sentinel-2 Satellite Imagery
12 Optimized Random Forest: A Hyperparameter Tuning Method in Machine Learning Algorithms
13 Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery
14 HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images
15 Efficient Transformer for Remote Sensing Image Segmentation
16 Hyper-LGNet: Coupling Local and Global Feature for Hyperspectral Image Classification
17 Hyper-ES2T: Efficient Spatial-Spectral Transformer for the Classification of Hyperspectral Remote Sensing Images
18 CCTNet: Coupling CNN and Transformer Networks for Crop Segmentation of Remote Sensing Images
19 Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery
20 Efficient DF-UNet: Dual-Flow Architecture for Wheat Yellow Rust Severity Detection by UAV Multispectral Imagery
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